Formats
Pascal VOC XML
Object Detection
IMPORTEXPORT
FEATURED
Object Detection

Pascal VOC XML

Pascal VOC is a common XML annotation format that is human readable but doesn't work with any known object detection models.

Overview

The annotation format originally created for the Visual Object Challenge (VOC) has become a common interchange format for object detection labels. It's well-specified and can be exported from many labeling tools including CVAT, VoTT, and RectLabel.

Unfortunately, no known models directly consume VOC XML labels. That's where Roboflow comes in; it's a universal computer vision format converter that can convert PASCAL VOC into any other format so your data is ready to train in a jiffy.

Format Description

Below, learn the structure of Pascal VOC XML.

img0001.xml
<annotation>
	<folder></folder>
	<filename>000001.jpg</filename>
	<path>000001.jpg</path>
	<source>
		<database>roboflow.ai</database>
	</source>
	<size>
		<width>500</width>
		<height>375</height>
		<depth>3</depth>
	</size>
	<segmented>0</segmented>
	<object>
		<name>helmet</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<occluded>0</occluded>
		<bndbox>
			<xmin>179</xmin>
			<xmax>231</xmax>
			<ymin>85</ymin>
			<ymax>144</ymax>
		</bndbox>
	</object>
	<object>
		<name>helmet</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<occluded>0</occluded>
		<bndbox>
			<xmin>112</xmin>
			<xmax>135</xmax>
			<ymin>145</ymin>
			<ymax>175</ymax>
		</bndbox>
	</object>
</annotation>

Split and Merge Datasets

With Roboflow supervision, an open source Python package with utilities for completing computer vision tasks, you can merge and split detections in Pascal VOC XML. Read our dedicated guides to learn how to merge and split Pascal VOC XML detections.

Split detections in Pascal VOC XML


import supervision as sv

ds = sv.DetectionDataset.from_pascal_voc(
    images_directory_path=f"dataset/train/images",
    annotations_directory_path=f"dataset/train/labels"
)

train_ds, test_ds = ds.split(split_ratio=0.7,
                             random_state=42, shuffle=True)
len(train_ds), len(test_ds)
# (700, 300)

Merge detections in Pascal VOC XML


import supervision as sv

ds_1 = sv.DetectionDataset.from_pascal_voc(
    images_directory_path=f"dataset1/train/images",
    annotations_directory_path=f"dataset1/train/labels"
)
len(ds_1)
# 100
ds_1.classes
# ['dog', 'person']

ds_2 = sv.DetectionDataset.from_pascal_voc(
    images_directory_path=f"dataset2/train/images",
    annotations_directory_path=f"dataset2/train/labels"
)
len(ds_2)
# 200
ds_2.classes
# ['cat']

ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
len(ds_merged)
# 300
ds_merged.classes
# ['cat', 'dog', 'person']

Supported Models

Below, see model architectures that require data in the Pascal VOC XML format when training a new model.

On each page below, you can find links to our guides that show how to plot predictions from the model, and complete other common tasks like detecting small objects with the model.

YOLOX

YOLOX is a high-performance object detection model.
Object Detection
Deploy with Roboflow
•
•
8.9k+
stars
•
Apache-2.0
license
•
Released
Jul 18, 2021

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Formats
Pascal VOC XML

What is Pascal VOC XML?

The annotation format originally created for the Visual Object Challenge (VOC) has become a common interchange format for object detection labels. It's well-specified and can be exported from many labeling tools including CVAT, VoTT, and RectLabel.

Unfortunately, no known models directly consume VOC XML labels. That's where Roboflow comes in; it's a universal computer vision format converter that can convert PASCAL VOC into any other format so your data is ready to train in a jiffy.

Deploy Models with Ease on Roboflow

With Roboflow, you can deploy a computer vision model without having to build your own infrastructure.

Get Started Today

Pascal VOC XML

Below, we show how to convert data to and from

Pascal VOC XML

. We also list popular models that use the

Pascal VOC XML

data format. Our conversion tools are free to use.

CONVERT To
CONVERT From
EXAMPLE

Convert Data to Pascal VOC XML

Use Roboflow to convert
Pascal VOC XML
to the following formats.

Roboflow is a trusted solution for converting and managing your data. Today, over 250,000 datasets are managed on Roboflow, comprised of 100 million labeled and annotated images.

With Roboflow, you get a solution with:
‍

Free data conversion

SOC II Type 2 Compliant

Trusted by 250,000+ developers

Pascal VOC XML
 to 
YOLOv8 PyTorch TXT
Pascal VOC XML
 to 
YOLOv8 Oriented Bounding Boxes
Pascal VOC XML
 to 
PaliGemma JSONL
Pascal VOC XML
 to 
YOLOv10 PyTorch TXT
Pascal VOC XML
 to 
YOLOv9 PyTorch TXT
Pascal VOC XML
 to 
Florence-2
Pascal VOC XML
 to 
Tensorflow TFRecord
Pascal VOC XML
 to 
COCO JSON
Pascal VOC XML
 to 
YOLO Darknet TXT
Pascal VOC XML
 to 
CreateML JSON
Pascal VOC XML
 to 
YOLOv7 PyTorch TXT
Pascal VOC XML
 to 
YOLOv5 PyTorch TXT
Pascal VOC XML
 to 
Sagemaker GroundTruth Manifest
Pascal VOC XML
 to 
Google Cloud AutoML Vision CSV
Pascal VOC XML
 to 
meituan/yolov6
Pascal VOC XML
 to 
Scaled-YOLOv4 TXT
Pascal VOC XML
 to 
YOLO Keras TXT
Pascal VOC XML
 to 
OpenAI CLIP Classification
Pascal VOC XML
 to 
RetinaNet Keras CSV
Pascal VOC XML
 to 
Tensorflow Object Detection CSV
Pascal VOC XML
 to 
Multiclass Classification CSV
Pascal VOC XML
 to 
YOLOv4 PyTorch TXT
Pascal VOC XML
 to 
YOLOv5 Oriented Bounding Boxes

Convert Data from Pascal VOC XML

Use Roboflow to convert the following formats to
Pascal VOC XML
format.

Roboflow is a trusted solution for converting and managing your data. Today, over 250,000 datasets are managed on Roboflow, comprised of 100 million labeled and annotated images.

With Roboflow, you get:
‍

Free data conversion

SOC II Type 1 Compliant

Trusted by 250,000+ developers

YOLOv8 PyTorch TXT
to
Pascal VOC XML
COCO JSON
to
Pascal VOC XML
CreateML JSON
to
Pascal VOC XML
YOLO Darknet TXT
to
Pascal VOC XML
Google Cloud AutoML Vision CSV
to
Pascal VOC XML
Sagemaker GroundTruth Manifest
to
Pascal VOC XML
SuperAnnotate JSON
to
Pascal VOC XML
LabelMe JSON
to
Pascal VOC XML
Supervisely JSON
to
Pascal VOC XML
LabelBox JSON
to
Pascal VOC XML
Scale AI JSON
to
Pascal VOC XML
VGG Image Annotator JSON
to
Pascal VOC XML
VGG Image Annotator CSV
to
Pascal VOC XML
VoTT CSV
to
Pascal VOC XML
VoTT JSON
to
Pascal VOC XML
LabelBox Video JSON
to
Pascal VOC XML
IBM Cloud Annotations JSON
to
Pascal VOC XML
Unity Perception JSON
to
Pascal VOC XML
YOLO Keras TXT
to
Pascal VOC XML
OIDv4 TXT
to
Pascal VOC XML
RetinaNet Keras CSV
to
Pascal VOC XML
OpenImages CSV
to
Pascal VOC XML
Tensorflow Object Detection CSV
to
Pascal VOC XML
Multiclass Classification CSV
to
Pascal VOC XML
Cogniac
to
Pascal VOC XML
Kaggle Wheat CSV
to
Pascal VOC XML
Udacity TXT
to
Pascal VOC XML
Marmot XML
to
Pascal VOC XML
Roboflow is a trusted solution for converting and managing your data. Today, over 100,000 datasets are managed on Roboflow, comprised of 100 million labeled and annotated images.

Once your data is in Roboflow, just add the link from your dataset and you're ready to go. We even include the code to export to common inference formats like TFLite, ONNX, and CoreML.

Below are pre-configured models that use the
Pascal VOC XML
data format
.

What computer vision models use Pascal VOC XML?

The

YOLOX

,

models all use the

Pascal VOC XML

data format.

YOLOX

YOLOX is a high-performance object detection model.

To see our entire list of computer vision models, check out the Roboflow Model Library.
img0001.xml
<annotation>
	<folder></folder>
	<filename>000001.jpg</filename>
	<path>000001.jpg</path>
	<source>
		<database>roboflow.ai</database>
	</source>
	<size>
		<width>500</width>
		<height>375</height>
		<depth>3</depth>
	</size>
	<segmented>0</segmented>
	<object>
		<name>helmet</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<occluded>0</occluded>
		<bndbox>
			<xmin>179</xmin>
			<xmax>231</xmax>
			<ymin>85</ymin>
			<ymax>144</ymax>
		</bndbox>
	</object>
	<object>
		<name>helmet</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<occluded>0</occluded>
		<bndbox>
			<xmin>112</xmin>
			<xmax>135</xmax>
			<ymin>145</ymin>
			<ymax>175</ymax>
		</bndbox>
	</object>
</annotation>
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